An important aspect in the Business Intelligence (BI) domain is to get the relevant information timely. Business Analysts rely on BI products to derive quality facts from huge volumes of historical and current data to make effective decisions. They increasingly depend on the search feature of a BI product to achieve this.
Different kinds of BI artifacts are present in a BI repository. The BI artifacts serve different purposes for different personas. For example, in SAP Business Objects, BI artifacts include Crystal Reports, Web Intelligence, Desktop Intelligence, Xcelsius, Pioneer, BI workspaces, and Information Spaces. Every BI artifact has its own format and purpose. There may be some BI artifacts which have saved data and others just metadata. Furthermore, different users end up creating the same content in different representations for the same data. Just as an example, the same data can be represented in different formats within the same type of document.
Under such a scenario, a search would return numerous BI artifacts having multiple formats. For conventional BI search engines, the ranking of these BI artifacts are based only on existence of data queried in reports, charts, tables and other BI artifacts and the number of terms matched.
However, such ranking techniques may be insufficient since it does not take into account of the different structures or content context of the BI artifacts. For example, a term may be matched in a portion of an artifact which is not important to a user as if it matched in another portion of an artifact. This may lead to a scenario where the former BI artifact is ranked higher than the latter, merely because more terms are matched. However, the higher ranking may lead the user to the wrong BI artifact. This may mislead the user to the non-relevant BI artifacts, which waste time, particularly with a search list having numerous BI artifacts.
Accordingly, there is a need to provide improved searching which allows more meaningful ranking to provide context to search results.